Use of multi-spectral visible and near-infrared satellite data for timely estimates of the Earth's surface reflectance in cloudy and aerosol loaded conditions: Part 1 - application to RGB image restoration over land with GOME-2

This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.

Downloads

Download Preprint

Authors

Joanna Joiner , Zachary Fasnacht , Wenhan Qin , Yasuko Yoshida , Alexander Vasilkov, Can Li, Lok Lamsal, Nickolay Krotkov 

Abstract

Space-based quantitative passive optical remote sensing of the Earth's surface typically involves the detection and elimination of cloud-contaminated pixels as an initial processing step. We explore a fundamentally different approach; we use machine learning with cloud contaminated satellite multi-spectral data to estimate underlying terrestrial surface reflectances at red, green, and blue (RGB) wavelengths. The NN reproduces land RGB reflectances with high fidelity even in scenes with moderate to high cloud optical thicknesses. This implies that spectral features of the Earth's surface can be detected and distinguished in the presence of clouds, even when they are partially obscured by clouds; the NN is able to separate the spectral fingerprint of the Earth's surface from that of the clouds, aerosols, gaseous absorption, and Rayleigh scattering, provided that there are adequately different spectral features and that the clouds are not completely opaque. Once trained, the NN enables rapid estimates of RGB reflectances with little computational cost. Aside from the training data, there is no requirement of prior information regarding the land surface spectral reflectance, nor is there need for radiative transfer calculations. We test different wavelength windows for reconstruction of surface reflectance. This work provides an initial example of a general approach that has many potential applications in land and ocean remote sensing as well as other practical uses such as in search and rescue, precision agriculture, and change detection.

DOI

https://doi.org/10.31223/X5JK6H

Subjects

Physical Sciences and Mathematics

Keywords

cloud-contamination, cloud-clearing, cloud removal, cloudy remote sensing, image reconstruction

Dates

Published: 2021-06-07 08:01

Last Updated: 2021-06-07 15:01

License

CC BY Attribution 4.0 International

Additional Metadata

Conflict of interest statement:
Authors ZF, WQ, YY, and AV are employed by Science Systems and Applications, Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Data Availability (Reason not available):
All data used in this study are publicly available. MCD43C NBAR are available from the NASA Level-1 and Atmosphere Archive and Distribution System Distributed Active Archive Center (LAADS DAAC) at https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD43C3/. GOME-2 radiance data are made available by the EUMETSAT project. They are mirrored and made available by the National Oceanic and Atmospheric Administration Comprehensive Large Array-Data Stewardship System (NOAA CLASS) at https://www.avl.class.noaa.gov/saa/products/welcome. Snow and ice data are available from the US National Ice Center and the National Snow and Ice Data Center Distributed Active Archive Center at https://doi.org/10.5067/3KB2JPLFPK3R and https://doi.org/10.7265/N52R3PMC.

Add a Comment

You must log in to post a comment.


Comments

There are no comments or no comments have been made public for this article.